Abstract
Publically available text-based documents (e.g. news, meeting transcripts) are a very important source of knowledge, especially for organizations. These documents mention domain entities such as persons, places, professional positions, decisions and actions. Querying these documents (instead of browsing, searching and finding) is a very relevant task for any person in general, and particularly for professionals dealing with intensive knowledge tasks. Querying text-based documents' data, however, is not supported by common technology. For that, such documents' content has to be explicitly and formally captured as facts into a knowledge base. Making use of automatic NLP processes for capturing such facts is a common approach, but their relatively low precision and recall give rise to data quality problems. Furthermore, facts existing in the documents are often insufficient to answer complex queries, thus the need to enrich the captured facts with facts from third-party repositories (e.g. public LOD). This paper describes the adopted process to clean, populate and enrich a knowledge base repository that is further exploited to answer complex queries. This process is triggered by a previous NLP parsing process and conducted by the (rich) ontology describing such repository.

Abstract
AEMOS is a simulator which aims to support the development of agent-based electronic markets capable of dealing with the natural semantic heterogeneity existent in this kind of environment. AEMOS simulates a marketplace which provides ontology matching services, enhanced with the exploitation of emergent social networks, enabling an efficient and transparent communication between agents, even when they use different ontologies. The system recommends possible alignments between the agent's ontologies, and lets them negotiate and decide which alignment should be used to translate the exchanged messages. In this paper we propose a new ontology alignment negotiation process, which promotes the reutilization and combination of already existent alignments, as well as the involvement of the business agents in the alignment composition process. With this new model, we aim to achieve a higher adequacy of the used alignments, as well as a more accurate and trustful evaluation of the alignments. © 2013 DIME UNIVERSITÀ DI GENOVA.

Abstract
Internally agents may use argumentation for both (i) reasoning about what to believe (i.e. theoretical reasoning) and/or (ii) for deciding what to do (i.e. practical reasoning). Despite existing differences between both, from a standpoint of first-personal reflection, a set of considerations for and against a particular conclusion are drawn on both [1]. On the other hand, concerning the types of agents' dialogues (e.g. Deliberation, Negotiation, Persuasion, Inquiry, Information-seeking dialogues), while a clear distinction between each one exist, most of the agents' dialogue occurrences involve mixtures of dialogue types. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
Soccer is a competitive and collective sport in which teammates try to combine the execution of basic actions (cooperative behavior) to lead their team to more advantageous situations. The ability to recognize, extract and reproduce such behaviors can prove useful to improve the performance of a team in future matches. This work describes a methodology for achieving just that makes use of a plan definition language to abstract the representation of relevant behaviors in order to promote their reuse. Experiments were conducted based on a set of game log files generated by the Soccer Server simulator which supports the RoboCup 2D simulated robotic soccer league. The effectiveness of the proposed approach was verified by focusing primarily on the analysis of behaviors which started from set-pieces and led to the scoring of goals while the ball possession was kept. One of the results obtained showed that a significant part of the total goals scored was based on this type of behaviors, demonstrating the potential of conducting this analysis. Other results allowed us to assess the complexity of these behaviors and infer meaningful guidelines to consider when defining plans from scratch. Some possible extensions to this work include assessing which plans have the ability to maximize the creation of goal opportunities by countering the opponent's team strategy and how the effectiveness of plans can be improved using optimization techniques.

Abstract
Vibrato is a frequency modulation effect of the singing voice and is very relevant in musical terms. Its most important characteristics are the vibrato frequency (in Hertz) and the vibrato extension (in semitones). In singing teaching and learning, it is very convenient to provide a visual feedback of those two objective signal characteristics, in real-time. In this paper we describe an algorithm performing vibrato detection and analysis. Since this capability depends on fundamental frequency (F0) analysis of the singing voice, we first discuss F0 estimation and compare three algorithms that are used in voice and speech analysis. Then we describe the vibrato detection and analysis algorithm and assess its performance using both synthetic and natural singing signals. Overall, results indicate that the relative estimation errors in vibrato frequency and extension are lower than 0.1%. © 2012 IEEE.

Abstract
Long-term travel time prediction (TTP) can be an important planning tool for both freight transport and public transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression (PPR), Support Vector Machine (SVM) and Random Forests (RF). For each one of these methods we study the best combination of input parameters. We also study the impact of different methods for the pre-processing tasks (feature selection, example selection and domain values definition) in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.